Extended multi-stream temporal-attention module for skeleton-based human action recognition (HAR)

Faisal Mehmood, Xin Guo, Enqing Chen, Muhammad Azeem Akbar, Arif Ali Khan +1 more
11/10/2024
cs.CV

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Abstract

Graph convolutional networks (GCNs) are an effective skeleton-based human action recognition (HAR) technique. GCNs enable the specification of CNNs to a non-Euclidean frame that is more flexible. The previous GCN-based models still have a lot of issues: (I) The graph structure is the same for all model layers and input data.

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Cite this paper

@article{mehmood2024extended,
  title  = {Extended multi-stream temporal-attention module for skeleton-based human action recognition (HAR)},
  author = {Faisal Mehmood and Xin Guo and Enqing Chen and Muhammad Azeem Akbar and Arif Ali Khan and Sami Ullah},
  year   = {2024},
  eprint = {2411.06553},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2411.06553v1}
}

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